A Survey of Federated Learning in Healthcare: Applications, Challenges, and Future Directions

This paper provides an extensive review of federated learning (FL) applications and challenges in the healthcare domain, highlighting its transformative role in enabling collaborative learning while addressing critical privacy and security concerns. FL, as a decentralized machine learning paradigm, allows multiple clients, such as hospitals or medical institutions, to collaboratively train models without sharing raw data. This capability makes FL particularly suitable for the healthcare sector, where sensitive patient information is governed by stringent privacy regulations and ethical considerations. The paper begins by introducing the fundamental concepts of FL, including its definition, architecture, and training process. It explains how FL differs from traditional centralized learning by keeping data localized while enabling a global model to benefit from diverse datasets. This characteristic is particularly valuable in healthcare, where data is often siloed across institutions and regions due to legal and operational constraints. The applications of FL in healthcare are categorized into three primary areas: classification tasks, segmentation tasks, and other specialized use cases. In classification tasks, FL has been employed for disease diagnosis models, such as predicting diabetes risk, detecting Alzheimer's disease, or identifying cancers. These applications demonstrate FL's ability to leverage distributed data for improving diagnostic accuracy. For segmentation tasks, FL is applied in medical image analysis, including tumor boundary delineation in MRI scans and lung nodule detection in CT scans. Additionally, FL enables the integration and analysis of electronic health records (EHRs) across institutions, enhancing data utility while ensuring compliance with privacy standards. However, the deployment of FL in healthcare is not without challenges. One major issue is data heterogeneity, where variations in data distributions across institutions (Non-IID data) can negatively affect model performance and convergence. Privacy and security concerns remain significant, as FL must ensure the confidentiality of local data and the security of model updates during training and aggregation processes. Another critical challenge is the high communication cost, especially in scenarios involving large-scale, multi-institutional collaborations. Frequent communication between clients and the central server or aggregators can introduce latency and increase resource demands. To address these challenges, the paper explores innovative solutions and future research directions. Techniques such as personalized federated learning algorithms and transfer learning approaches are proposed to tackle data heterogeneity. Privacy-preserving mechanisms, including differential privacy, secure multi-party computation, and homomorphic encryption, are highlighted to ensure robust data protection. Communication efficiency can be improved through advanced aggregation methods like hierarchical FL and compression techniques. These innovations are expected to reduce communication overhead and enhance scalability in practical implementations. Results from existing studies demonstrate the effectiveness of FL in improving healthcare outcomes. For example, FL has achieved high accuracy in multi-site breast cancer classification, improved disease prediction models, and enabled the secure integration of EHR data. These advancements showcase FL's potential to revolutionize medical research and practice by fostering cross-institutional collaboration while maintaining patient confidentiality. In conclusion, this review emphasizes FL's pivotal role in addressing critical challenges in healthcare data analysis and collaborative modeling. By leveraging its unique features and addressing its limitations, FL is well-positioned to drive significant advancements in disease diagnosis, personalized treatments, and large-scale medical research. This work serves as a foundation for future studies, advocating for continued innovation to meet the evolving needs of the healthcare industry.